Summary of Partial Identifiability in Inverse Reinforcement Learning For Agents with Non-exponential Discounting, by Joar Skalse and Alessandro Abate
Partial Identifiability in Inverse Reinforcement Learning For Agents With Non-Exponential Discounting
by Joar Skalse, Alessandro Abate
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper aims to address a fundamental challenge in inverse reinforcement learning (IRL), where multiple preferences can lead to the same observed behavior. The authors focus on non-exponential discounting, particularly hyperbolic discounting, which is more representative of human behavior than exponential discounting. They show that IRL alone cannot infer enough information about the reward function to identify the optimal policy for agents with non-exponential discounting, making it insufficient for characterizing their preferences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper investigates how machines can learn what people like or want by observing their actions. The problem is that there are many possible reasons why someone might do something, and figuring out which one is correct is hard. The authors look at a specific way that humans think about the future, where they value things more when they’re closer, rather than equally over time. They show that current methods for inferring what people like or want from observing their actions don’t work well with this kind of thinking. This has important implications for how we design machines to understand human behavior. |
Keywords
» Artificial intelligence » Reinforcement learning
